The List / Categories
Retrieval-augmented financial systems combine search with generation over filings, news and other sources. This guide focuses on evidence selection, temporal grounding and answer traceability.
Read financial QA and reports papers alongside retrieval work to connect evaluation to realistic document tasks.
The paper introduces a retrieval-augmented instruction-tuned LLM framework for financial sentiment analysis that significantly outperforms traditional models and general-purpose LLMs by combining targeted fine-tuning with external context retrieval.
The paper demonstrates that structurally aware chunking of SEC financial reports using the Chipper document understanding model significantly improves Retrieval Augmented Generation (RAG) question-answering accuracy and retrieval consistency compared to fixed-size token chunking.
AlphaFin introduces a comprehensive dataset and the Stock-Chain framework, which combines LoRA-finetuned LLMs with Retrieval-Augmented Generation to achieve state-of-the-art stock trend prediction accuracy and a 30.8% annualized return while providing interpretable financial analysis.
OmniEval introduces an omnidirectional, automatic RAG benchmark for finance using a matrix-based topic-task evaluation framework, multi-agent data generation, and fine-tuned LLM evaluators to reveal significant performance gaps in vertical domain RAG systems.
This study evaluates Retrieval-Augmented Generation (RAG) systems for answering questions about bank financial reports, finding that high-quality embedding and generation models significantly improve context relevance and answer faithfulness, particularly for qualitative inquiries.
The paper introduces ICKG, a fine-tuned LLM for generating dynamic knowledge graphs from financial news, and KGTransformer, an attention-based GNN that leverages meta-entities to outperform baselines in link prediction and thematic AI investing.
RA-CFGPT introduces a retrieval-augmented Chinese financial assistant that fine-tunes a large language model on a hybrid knowledge base to improve accuracy, compliance, and risk assessment in financial question answering and document analysis.
FinSearchComp is an open-source benchmark of 635 expert-annotated questions evaluating LLM agents' ability to perform realistic financial search and reasoning across time-sensitive, historical, and complex investigative tasks.
The paper introduces FinDER, a benchmark dataset of 5,703 expert-annotated query-evidence-answer triplets derived from S&P 500 10-K filings to evaluate Retrieval-Augmented Generation (RAG) systems on ambiguous, real-world financial queries, demonstrating that neural retrievers and LLM-based reranking significantly outperform traditional methods but that factual correctness remains challenging without high-quality context.
Discovered through citation/reference expansion; connected to 2 source papers.
The paper introduces a three-phase RAG pipeline for financial question answering that combines query expansion, markdown restructuring, fine-tuned hybrid retrieval, and DPO-optimized generation, achieving significant NDCG@10 improvements across seven financial benchmarks.
FinReflectKG introduces an agentic, reflection-driven framework for constructing large-scale financial knowledge graphs from S&P 100 SEC 10-K filings, achieving superior extraction quality and schema compliance compared to single-pass and multi-pass baselines.
MultiFinRAG is a multimodal RAG framework that extracts structured data from financial PDFs and uses tiered retrieval to achieve 75.3% accuracy on complex QA tasks, outperforming ChatGPT-4o by 19 percentage points.
The paper proposes HiREC, a hierarchical retrieval and evidence curation framework that improves open-domain financial question answering on standardized SEC filings by reducing near-duplicate confusion and iteratively filling information gaps, evaluated on the new LOFin benchmark.
This paper proposes a metadata-driven multi-stage RAG architecture that leverages LLM-generated metadata for pre-retrieval filtering, contextual chunk embedding, and custom reranking, achieving superior financial question-answering performance on FinanceBench compared to baseline and advanced RAG configurations.
Discovered through citation/reference expansion; connected to 5 source papers.
Fin-RAG is a multimodal Retrieval-Augmented Generation system for financial documents that combines GPT-4, BERT embeddings, and CoBERT re-ranking to achieve high accuracy in natural language querying of text and image-based financial reports.
FinKario introduces an automated, event-enhanced financial knowledge graph and a two-stage graph-based RAG retrieval strategy that significantly outperforms financial LLMs and institutional strategies in stock trend prediction and backtesting accuracy.
FinBloom introduces a knowledge-grounding framework using a fine-tuned 7B parameter LLM as a Financial Agent to retrieve real-time structured and textual data, enabling accurate, low-latency responses to dynamic financial queries without relying on traditional dense-vector RAG.
Discovered through citation/reference expansion; connected to 3 source papers.
The paper introduces FLEX, a retrieval-augmented framework that enriches financial sentences with explicit knowledge using LLMs, significantly improving financial sentiment analysis accuracy across zero-shot, few-shot, and fine-tuning settings.
FinVault introduces the first execution-grounded security benchmark for financial agents, revealing that state-of-the-art LLMs remain highly vulnerable to semantic and regulatory attacks in sandboxed environments with state-writable databases.
Discovered through citation/reference expansion; connected to 2 source papers.
AlphaPROBE introduces a DAG-based framework for alpha mining that uses a Bayesian Factor Retriever and a DAG-aware LLM generator to discover diverse, high-performing factors on Chinese stock markets.
The paper introduces a domain-fine-tuned page scorer for financial RAG that significantly improves page and chunk retrieval accuracy by treating pages as intermediate retrieval units, closing the gap between document discovery and precise context localization.
FinVision is a multi-agent LLM framework that integrates news, visual technical analysis, and historical reflection to generate explainable trading decisions with position sizing, outperforming traditional and RL baselines on major tech stocks.
The paper introduces NatureKG, a domain-specific knowledge graph for nature finance, and demonstrates the feasibility of using fine-tuned small LLMs like Phi-3 to translate natural language into Cypher queries for structured environmental risk assessment.
Discovered through citation/reference expansion; connected to 1 source papers.